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Energy Storage Technologies RESTORATIVE is a pioneering Marie Skłodowska-Curie Actions (MSCA) Doctoral Network dedicated to accelerating the green transition through Thermo-Mechanical Grid-Scale Energy
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, potentially accelerating design. We hypothesize that combining GM with AI can democratize software development, enabling broader participation beyond traditional programmers. Integrating this approach with
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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant
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identify radio signal features that predict future outcomes and use them to guide and accelerate learning? This PhD project addresses this question by studying how agents can: extract radio-derived features
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imaging of energy storage materials, with the focus on detection of light elements and monitoring of electrochemical processes. The research will be conducted primarily at the accelerator facility
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representations of their environment. These features carry semantic meaning relevant to the agents’ mission and can accelerate decision-making, enhance ambient awareness, and enable prediction of action outcomes